import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt

img1 = cv.imread('/Users/wanggh/Desktop/icon.png', cv.IMREAD_GRAYSCALE)  # queryImage
img2 = cv.imread('/Users/wanggh/Desktop/a.jpeg', cv.IMREAD_GRAYSCALE)  # trainImage
# img2 = cv.imread('/Users/wanggh/Desktop/original.jpeg', cv.IMREAD_GRAYSCALE)  # trainImage
# 初始化SIFT描述符
sift = cv.SIFT_create()
# 基于SIFT找到关键点和描述符
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
flann = cv.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1, des2, k=2)
# ＃根据Lowe的比率测试存储所有符合条件的匹配项。
good = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good.append(m)
if len(good) > 10:
    src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
    dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
    M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC, 5.0)
    matchesMask = mask.ravel().tolist()
    h, w, d = img1.shape
    pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)
    dst = cv.perspectiveTransform(pts, M)
    img2 = cv.polylines(img2, [np.int32(dst)], True, 255, 3, cv.LINE_AA)
else:
    print("Not enough matches are found - {}/{}".format(len(good), 10))
    matchesMask = None
# cv.drawMatchesKnn将列表作为匹配项。
draw_params = dict(matchColor=(0, 0, 255),  # 用绿色绘制匹配
                   singlePointColor=None,
                   matchesMask=matchesMask,  # 只绘制内部点
                   flags=2)
img3 = cv.drawMatches(img1, kp1, img2, kp2, good, None, **draw_params)
cv.imshow("test", img3)
cv.waitKey(0)
cv.destroyAllWindows()
